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Fuzzy logic-based predictive model for biomass pyrolysis

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  • Lerkkasemsan, Nuttapol

Abstract

As pyrolysis reaction is one of an important reaction applied to lignocellulosic biomass in order to transform it to be user-friendly energy form recognized as prospective alternative energy source, the reaction has been widely investigated in order to understand the mechanisms and kinetics of the pyrolysis. However, modeling pyrolysis of biomass is full of complication. As lignocellulosic biomass is not a homogeneous chemical source, chemical compositions in biomass are also uncertain and they vary even in the same biomass. The reactions of imprecise chemical compositions in biomass affects the capability of deterministic model in modeling chemical reaction since available deterministic models are designed to model homogeneous and precise chemical compositions. With this problem, it raises the idea of using model which has ability to calculate something ambiguous. Since the fuzzy logic-based model which is adaptive network-based fuzzy inference system (ANFIS) is built to calculate uncertainty, the model should be suitable to handle uncertainty which is imprecise chemical compositions in the reaction. The proposed model is built with four input variables: the reaction time, amount of cellulose component, amount of hemicellulose component, and amount of lignin component in biomass. The model is trained with tuning datasets which are the pyrolysis datasets of lignin, cellulose and Madhuca before applying to predict the pyrolysis reactions of Pongamia pinnata and Jatropha curcas. The comparative results show that the proposed model can correctly predict 91.82% and 97.29%, respectively, of the pyrolysis reactions of P. pinnata and J. curcas. As the ANFIS model gives good prediction in modeling pyrolysis of two different biomasses, the model can be applied to predict the pyrolysis reaction of other lignocellulosic biomass products.

Suggested Citation

  • Lerkkasemsan, Nuttapol, 2017. "Fuzzy logic-based predictive model for biomass pyrolysis," Applied Energy, Elsevier, vol. 185(P2), pages 1019-1030.
    • Handle: RePEc:eee:appene:v:185:y:2017:i:p2:p:1019-1030
    DOI: 10.1016/j.apenergy.2016.02.105
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    References listed on IDEAS

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    1. Corral, Serafín & Legna-de la Nuez, David & Romero-Manrique de Lara, David, 2015. "Integrated assessment of biofuel production in arid lands: Jatropha cultivation on the island of Fuerteventura," Renewable and Sustainable Energy Reviews, Elsevier, vol. 52(C), pages 41-53.
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    4. Sharma, Rajeev & Sheth, Pratik N. & Gujrathi, Ashish M., 2016. "Kinetic modeling and simulation: Pyrolysis of Jatropha residue de-oiled cake," Renewable Energy, Elsevier, vol. 86(C), pages 554-562.
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    1. Pomeroy, Brett & Grilc, Miha & Likozar, Blaž, 2022. "Artificial neural networks for bio-based chemical production or biorefining: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 153(C).
    2. Peters, Jens F. & Banks, Scott W. & Bridgwater, Anthony V. & Dufour, Javier, 2017. "A kinetic reaction model for biomass pyrolysis processes in Aspen Plus," Applied Energy, Elsevier, vol. 188(C), pages 595-603.
    3. Zhang, Huiyan & Ma, Yuna & Shao, Shanshan & Xiao, Rui, 2017. "The effects of potassium on distributions of bio-oils obtained from fast pyrolysis of agricultural and forest biomass in a fluidized bed," Applied Energy, Elsevier, vol. 208(C), pages 867-877.
    4. Zhang, Jiyuan & Tang, Hailong & Chen, Min, 2019. "Linear substitute model-based uncertainty analysis of complicated non-linear energy system performance (case study of an adaptive cycle engine)," Applied Energy, Elsevier, vol. 249(C), pages 87-108.
    5. Böhler, Lukas & Krail, Jürgen & Görtler, Gregor & Kozek, Martin, 2020. "Fuzzy model predictive control for small-scale biomass combustion furnaces," Applied Energy, Elsevier, vol. 276(C).

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